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Record W2145358785 · doi:10.5539/ass.v9n3p7

China’s Low Income Urban Housing

2013· article· en· W2145358785 on OpenAlexvenueno aff
Ian G. Cook, Chaolin Gu, Jamie P. Halsall

Bibliographic record

VenueAsian Social Science · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsChinaCommunismLeasehold estateRentingSocialismRedevelopmentWork (physics)Economic growthPopulationBusinessPolitical scienceEconomicsPoliticsSociologyLaw

Abstract

fetched live from OpenAlex

In this paper, firstly we briefly outline the historical legacies of inner-city housing that are the focus for redevelopment today, and then summarise the legacy of mass housing built by the work-unit or danwei in the Maoist era. The bulk of the paper, however, is then concerned with the switch to privatisation (via ‘market socialism with Chinese characteristics’) in the Reform Period under Deng Xiaoping and his successors, and the role of land policy that forms an important constraint on housing provision. Demand for low-income housing is in part due to the continuation of the Maoist hukou registration system which acts as a major barrier to full participation in the housing market and consequently China’s cities now have a huge migrant non-hukou ‘floating population’ (liudong renkou) that must be housed via alternative means, preferably as cheaply and effectively as possible. There is also the situation of the ‘ant tribe’ of young low-paid college graduates to consider. For people like these renting in overcrowded conditions is one option, but because of China’s unique development trajectory, (driven by the Chinese Communist Party since the founding of the People’s Republic of China in 1949, self-help housing is not a major form of housing provision, therefore there are few examples to consider. Hence, we discuss several examples of China’s low income urban housing.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.002
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.262
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2013
Admission routes1
Has abstractyes

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